In a multi-cell scenario, a network user (e.g. a UE in the 3GPP LTE system) will not only receive signal from a serving cell, but will also receive interfering signals from the other cells of the communication system. 3GPP proposes a frequency reuse factor of 1 in LTE systems which means that interference will generally be high. In the downlink, a UE will suffer from interference signals from neighboring e-NodeB(s). In the uplink, an e-NodeB will suffer from interference signals from other UEs in neighboring cells which are assigned to the same frequency radio resources. FIG. 1 illustrates the downlink interference and uplink interference in such a communication system.
The downlink/uplink interference, as illustrated in FIG. 1, will degrade the receiver performance. To achieve optimal receiver performance joint detection of the designated signal and the interference signals can be performed. However, such joint detection is usually not feasible since necessary information for decoding the interference signals with joint decoding usually is unavailable, such as rank indicator (RI), precoding matrix indicator (PMI), modulation and coding scheme (MCS), radio resource allocation, etc. The lack of necessary information also makes detection and cancellation of the interference signals very difficult.
By measuring/estimating the statistics of the interferences the impact of the interference can significantly be mitigated. For example, it is well known that Minimum Mean Square Error (MMSE) receivers utilizing the estimated covariance matrix of the interference plus noise can significantly improve the throughput (TPUT) performance. Such MMSE receivers are e.g. called MMSE IRC (Interference Rejection Combining) receivers. The covariance matrix can also be used in other types of receivers. For example, in maximum likelihood (ML) or quasi-ML receivers, this covariance matrix is used for pre-whitening the interference plus noise.
In general, the better the statistics of the interferences can be measured/estimated, the better receiver performance can be achieved.
Several non-iterative methods for estimating the covariance matrix of interference plus noise are known in the art. However, the performance of such non-iterative methods is not satisfactory.
Another approach is to use iterative methods for estimating the covariance of the interference. For example, based on the log likelihood ratios (LLRs) output from either a turbo decoder or a multiple-input and multiple-output (MIMO) detector, the transmitted signals from the serving cell can be regenerated, which are usually called soft-symbols. These symbols can be subtracted from the received signals for estimating the covariance matrix of the interference plus noise as,(k,l)=(y(k,l)−(k,l)(k,l))(y(k,l)−(k,l)(k,l))H,
where (k,l) is the regenerated soft symbols, and (k,l) is the covariance matrix estimate based on the data for a single data resource element (RE). However, since the (k,l) is very noisy most methods will average the above covariance matrix samples to get better estimation quality.